diff options
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/algorithms/FISTA.py | 78 |
1 files changed, 39 insertions, 39 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/FISTA.py b/Wrappers/Python/ccpi/optimisation/algorithms/FISTA.py index ee51049..d509621 100755 --- a/Wrappers/Python/ccpi/optimisation/algorithms/FISTA.py +++ b/Wrappers/Python/ccpi/optimisation/algorithms/FISTA.py @@ -57,21 +57,21 @@ class FISTA(Algorithm): # algorithmic parameters if opt is None: - opt = {'tol': 1e-4, 'memopt':False} + opt = {'tol': 1e-4} - self.tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 - memopt = opt['memopt'] if 'memopt' in opt.keys() else False - self.memopt = memopt +# self.tol = opt['tol'] if 'tol' in opt.keys() else 1e-4 +# memopt = opt['memopt'] if 'memopt' in opt.keys() else False +# self.memopt = memopt # initialization - if memopt: - self.y = x_init.copy() - self.x_old = x_init.copy() - self.x = x_init.copy() - self.u = x_init.copy() - else: - self.x_old = x_init.copy() - self.y = x_init.copy() +# if memopt: + self.y = x_init.copy() + self.x_old = x_init.copy() + self.x = x_init.copy() + self.u = x_init.copy() +# else: +# self.x_old = x_init.copy() +# self.y = x_init.copy() #timing = numpy.zeros(max_iter) #criter = numpy.zeros(max_iter) @@ -85,37 +85,37 @@ class FISTA(Algorithm): # algorithm loop #for it in range(0, max_iter): - if self.memopt: +# if self.memopt: # u = y - invL*f.grad(y) # store the result in x_old - self.f.gradient(self.y, out=self.u) - self.u.__imul__( -self.invL ) - self.u.__iadd__( self.y ) - # x = g.prox(u,invL) - self.g.proximal(self.u, self.invL, out=self.x) - - self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) - - # y = x + (t_old-1)/t*(x-x_old) - self.x.subtract(self.x_old, out=self.y) - self.y.__imul__ ((self.t_old-1)/self.t) - self.y.__iadd__( self.x ) - - self.x_old.fill(self.x) - self.t_old = self.t - - - else: - u = self.y - self.invL*self.f.gradient(self.y) - - self.x = self.g.proximal(u,self.invL) - - self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) + self.f.gradient(self.y, out=self.u) + self.u.__imul__( -self.invL ) + self.u.__iadd__( self.y ) + # x = g.prox(u,invL) + self.g.proximal(self.u, self.invL, out=self.x) + + self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) + + # y = x + (t_old-1)/t*(x-x_old) + self.x.subtract(self.x_old, out=self.y) + self.y.__imul__ ((self.t_old-1)/self.t) + self.y.__iadd__( self.x ) + + self.x_old.fill(self.x) + self.t_old = self.t - self.y = self.x + (self.t_old-1)/self.t*(self.x-self.x_old) - self.x_old = self.x.copy() - self.t_old = self.t +# else: +# u = self.y - self.invL*self.f.gradient(self.y) +# +# self.x = self.g.proximal(u,self.invL) +# +# self.t = 0.5*(1 + numpy.sqrt(1 + 4*(self.t_old**2))) +# +# self.y = self.x + (self.t_old-1)/self.t*(self.x-self.x_old) +# +# self.x_old = self.x.copy() +# self.t_old = self.t def update_objective(self): self.loss.append( self.f(self.x) + self.g(self.x) )
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